An Effective Method for Classification of High Dimensional Data
Benson S. Y. Lam, Hong Yan · 2007
We study a new high dimensional data problem in this paper. In pattern classification, if many dimensions of two groups share a similar distribution, the classification error rates will be 50%. We have proposed a new clustering algorithm to deal with this problem. Its basic idea is to confine the support of the optimization equation so that the data points in one group can only have small contribution to the estimated cluster center in another group. Experiments show that the proposed method is able to yield good results in eight real world data sets and its performance is better than 10 existing methods.